Machine Learning with DNN is a popular tool and highly successful in solving tasks in many fields of application [1]}.
However, the associated increase of DNN complexity in terms of the number of network layers or of neurons in each layer severely complicates the understanding of how DNN solve their learned task [2]}.
To improve the explainability of DNN, we transfer methods for analyzing complex and opaque systems from the field of neuroscience, which has been studying the brain over decades.
In this work, we focus on adapting how brain activity is typically visualized as a topographic map.
For example, brain activity recorded through EEG measurements [3]} is represented as a top view of the head with a superimposed topographic map of neural activity [4]}.
Adapting this approach to be applicable to DNN can help to visualize and understand their internal processes more intuitively, too.
However, in contrast to the brain, DNN typically do not have an informative order of neurons because there are no connections between neurons within the same layer.
Therefore, to be able to visualize activations of neurons in DNN as topographic maps, we research techniques to layout the neurons in a two-dimensional space in which neurons of similar activity are in the vicinity of each other.
The idea of introducing a topographic neuron layout in artificial neural networks is not novel.
SOM [5]} follow a similar motivation and already constrain the neurons to form a topographical layout during training.
However, traditional SOM are only shallow neural networks and more recent approaches on training deep SOM [6]} have not gained popularity yet.
Most DL models that are used in practice are implemented without a topographical layout of the neurons.
Our aim is to provide the possibility to create a topographic layout visualization for any DNN, particularly those that are already trained and potentially deployed in the real world.
